To graduate from the program, a student must take and pass ten one-semester courses, of which 5 are core (or compulsory) courses. Each course is assigned a number of credit units (CU). Usually, a one-semester course corresponds to 3 CU and comprises 13 three-hour classes. Assessments are based on assignments, projects, exams, presentations, and class participation.
The program may be offered full-time (1 year), part-time (2 years), or combined mode. It is possible if full-time students who wish to graduate in October instead of July in the second year. Students graduating after the summer are required to schedule a summer course (the 10th course).
1. Programme Core Courses (12 credit units)
| Course Code | Course Title | Level | Credit Units |
|---|---|---|---|
| IS5413 | Database Management Systems | P5 | 3 |
| IS6335 | Data Visualization | P6 | 3 |
| MS5217 | Statistical Data Analysis | P5 | 3 |
| MS6711 | Data Mining | P6 | 3 |
2. Stream Core and Elective Courses (18 credit units)
Students are required to complete the following stream core course:
Stream Core Course (3 credit units)
| Course Code | Course Title | Level | Credit Units |
|---|---|---|---|
| MS5218 | Applied Linear Statistical Models | P5 | 3 |
and 15 credits of elective courses with at least 12 credits chosen from the following stream elective course list. The remaining course may be chosen from the postgraduate elective courses offered by any department in the College of Business.
Stream Elective Courses (15 credit units)
| Course Code | Course Title | Level | Credit Units |
|---|---|---|---|
| MS5216 | Decision Analytics | P5 | 3 |
| MS5223 | Project Management | P5 | 3 |
| MS5313 | Managerial Decision Modeling | P5 | 3 |
| MS5318 | Predictive Analytics with Excel and R | P5 | 3 |
| MS6211 | Statistical Modelling in Risk Management | P6 | 3 |
| MS6219 | Predictive Modeling and Forecasting for Business | P6 | 3 |
| MS6221 | Predictive Modeling in Marketing | P6 | 3 |
| MS6601 | Statistical Modelling in Economics and Finance | P6 | 3 |
| MS6712 | Contemporary Topics in Quantitative Analysis for Business | P6 | 3 |
1. Programme Core Courses (12 credit units)
| Course Code | Course Title | Level | Credit Units |
|---|---|---|---|
| IS5413 | Database Management Systems | P5 | 3 |
| IS6335 | Data Visualization | P6 | 3 |
| MS5217 | Statistical Data Analysis | P5 | 3 |
| MS6711 | Data Mining | P6 | 3 |
2. Stream Core and Elective Courses (18 credit units)
Students are required to complete the following stream core course:
Stream Core Course (3 credit units)
| Course Code | Course Title | Level | Credit Units |
|---|---|---|---|
| MS5218 | Applied Linear Statistical Models | P5 | 3 |
and 15 credits of elective courses with at least 12 credits chosen from the following stream elective course list. The remaining course may be chosen from the postgraduate elective courses offered by any department in the College of Business.
Stream Elective Courses (15 credit units)
| Course Code | Course Title | Level | Credit Units |
|---|---|---|---|
| MS5216 | Decision Analytics | P5 | 3 |
| MS5223 | Project Management | P5 | 3 |
| MS5313 | Managerial Decision Modeling | P5 | 3 |
| MS5318 | Predictive Analytics with Excel and R | P5 | 3 |
| MS6211 | Statistical Modelling in Risk Management | P6 | 3 |
| MS6219 | Predictive Modeling and Forecasting for Business | P6 | 3 |
| MS6221 | Predictive Modeling in Marketing | P6 | 3 |
| MS6601 | Statistical Modelling in Economics and Finance | P6 | 3 |
| MS6712 | Contemporary Topics in Quantitative Analysis for Business | P6 | 3 |
| Course Code | Description |
|---|---|
| IS5413 | This course aims to introduce the basic concepts of database systems. It covers the methods and tools for the conceptual and logical design of database applications, and relational database models and languages for the physical design and implementation of database systems. |
| IS6335 | The goal of this course is to learn how to use visualization tools for data interpretation under the business context. We will explore ways to organize and derive meaning from vast amounts of data, with interesting visual examples from different application areas. Students will learn concepts, methods, and applications of data visualization methods. Students will also learn visualization tools from GUI-based Tableau software to more advanced programmable visualization packages in R and Python. They will be guided in creating engaging and interactive visualizations, as well as experiencing virtual reality applications. Students will apply the concepts and skills to designing a final project. |
| MS5217 | This course covers fundamental statistical concepts and necessary computational tools in data analysis. The goal is to learn how to perform descriptive, analytical, and predictive data analysis based on real-world problems. This course also serves as a quantitative foundation for elective courses in marketing, finance, economics, and more advanced data science courses. |
| MS5218 | The aims of this course are to introduce the statistical concepts and methodology of linear statistical models. The curriculum emphasizes the use of regression modeling and analysis of variance techniques in solving business problems. Develop students’ analytic ability to integrate and apply the knowledge and quantitative skills, in particular linear statistical model methods, gained in the course to solve business problems. Provide students with the opportunity to develop their skills in presenting the findings of their own project. |
| MS6711 |
This course introduces students to a range of popular and practical data mining and machine learning algorithms relevant to business applications. Students are required to perform data analysis using the python programming language. Upon successful completion of this course, students will have acquired the core foundational knowledge in the field, and be well-prepared for a wide variety of careers in data-analytics. |
| MS5216 | This course aims to train students’ skills in modelling and optimization that are essential in turning real-world business decision-making problems into mathematical models and developing solution methods using computer packages such as spreadsheets, R/Python. It serves as a foundation course for business analytics, and covers commonly used optimization methods in business applications, including linear programming, and nonlinear optimization. It also introduces application of the optimization methods to a wide range of problems, including statistical estimation, machine learning, and business decision making under uncertainty. |
| MS5223 |
This course aims to introduce fundamental concepts of project management, with an emphasis on the trade-offs involved; provide students with the tools and methodologies developed to assist project managers; enable students to apply the concepts and tools of project management through assignments, project, and case studies. |
| MS5313 |
Serving as a foundation course for developing advanced analytical and planning skills, this course aims to sharpen students’ ability to creatively design, formulate, and construct quantitative models for managerial decision problems. Specifically, this course is intended to provide students with the key concepts, knowledge, and tools to use data, analytical models and information technology to support practical managerial decision-making. Develop students’ basic skills and hands-on experiences to uncover useful information and to analyse various business decision problems. Expose students to the practical cases of how quantitative modelling and analysis skills have generated significant business values and competitive advantages. |
| MS5318 | The aim of this course is to introduce the statistical concepts and methodologies that are often associated with making predictions with data. We begin with fundamental statistical analysis (e.g. inference, simple regression), then adds both breadth (e.g. logistic regression) and depth (e.g. model selection) to the use of regression to find the best prediction model for business forecasting. You will learn how to build predictive models with data sets in various structures (e.g. quantitative or categorical response/predictors). You will understand the trade-off between over-predicting versus under-predicting. You will practice utilizing the learned methods to solve data-based business decision problems (e.g. healthcare operations, fraud detection) through examples and case studies. R language will be used to process data and generate prediction models. No prior statistical knowledge is required, and you do not need prior knowledge about Excel or R. |
| MS6211 |
This course aims to prepare students with business knowledge of risk management with emphasis on operational risk management, credit risk management, and financial risk management; develop students’ modelling and computing skills to create and evaluate credit scorecards. |
| MS6219 | This course aims to introduce students to a range of forecasting techniques used in business and economics; develop a solid conceptual understanding of these techniques; enable students to appreciate the practical relevance of the techniques through case studies; acquaint students with the necessary computing knowledge to execute an analysis. |
| MS6221 | The goal of the class is to provide a broad overview of modern data-driven marketing techniques. We will cover the main areas of marketing that require data-driven decisions — targeted promotions and advertisements, churn management, recommender systems, pricing, and demand prediction. The emphasis is on applied predictive modeling in Python, and how machine learning tools are employed in the data science industry. The prerequisites include one course in probability and statistics and one course in regression analysis. Students are expected to work at least 5 hours after every lecture. |
| MS6601 | The goal of the class is to introduce financial econometrics: the intersection of statistics and asset pricing. We will cover a wide range of topics, including linear and nonlinear time series, volatility modeling, multivariate time series, and factor models. Particularly, we will discuss how factor-based investing and machine learning are employed in the investment industry. The prerequisites include one course in probability and statistics, one course in regression analysis, and basic knowledge in time series models. Students are expected to work at least 5 hours after every lecture. |
| MS6712 | This course aims to extend the knowledge of students in the use of quantitative analysis and to further develop students the practical skills of some advanced quantitative techniques for business decision problems. |
